[Numpy-discussion] Re: my Numpy statements are slower than indexed formulations in some cases

[Rob <ro...@py...>]

Rob wrote:
> 
> I have a number of thse routines in some EM code.  I've tried to
> Numpyize them, but end up with code that runs even slower.
> 
> Here is the old indexed routing:
> 
> -----------------
> for JJ in range(0,TotExtMetalEdgeNum):
> 
>     McVector+=Ccc[0:TotExtMetalEdgeNum,JJ] * VcVector[JJ]
> ----------------
> 
> Here is the Numpy version:
> 
> ---------------------
> McVector= add.reduce(transpose(Ccc[...] * VcVector[...]))
> ---------------------
> 
> I wonder if there is another faster way to do this?  Thanks,  Rob.
> 
> --

I did speed things up just a tiny bit by using:

add.reduce(Ccc*VcVector,1) instead of
add.reduce(transpose(Ccc*VcVector).

But I'm still running way slower than an indexed array scheme.   Rob.
> The Numeric Python EM Project
> 
> www.pythonemproject.com

-- 
The Numeric Python EM Project

www.pythonemproject.com